rust VS py-spy

You can use Tensorflow for Rust to simplify that task and avoid pain with regex. Just have the right mindset.

What is the benefit of this compared to using bindings/a wrapper to Tensorflow, or other ML libraries written in C/C++, such as this community hosted project on tensorflow's github. If it's just for fun that is a valid enough reason imo, just curious since you describe it as a better Tensorflow because of the typing vs using the python wrapper, when there already exist ways to interact with tensorflow with both Rust and other statically typed languages, also including C++ (officially supported), C#, Haskell and Scala, as well as probably having bindings not mentioned on the documentation for more niche languages.

(3) You could use TensorFlow as your executor: https://github.com/tensorflow/rust

TensorFlow has language support for TypeScript well as Rust.

Thank you for the restponse! With tensorflow I am probably better of with something like; [tensorflow rust bindings](https://github.com/tensorflow/rust/tree/master/src). But I believe some useful extensions are still written in python for example; [TFDV](https://github.com/tensorflow/data-validation).. and how about scikit-learn or even something that is simpler like fb-prophet that is entirely written in python?

Tensorflow bindings exist, technically, but they're in a pretty rough state AFAIK.

tensorflow/rust (Rust): Rust language bindings for TensorFlow

For CPU cycles, py-spy[0] is getting more and more used. For RAM, I would like to known too...

[0] -- https://github.com/benfred/py-spy

Theres also Py Spy, a profiling tool that can generate flame charts containing a mix of python and C (or C++) calls.

https://github.com/benfred/py-spy

It's worked really well for my needs

You should profile your consumer to check the bottlenecks. You can use the excellent py-spy(written in Rust). IMO a few usage of Numba there and there should solve your performance issues.

So as a first step you'll want to profile your program to figure out where it's slow, and hopefully that'll also tell you why it's slow. I'm the (biased) author of the Sciagraph profiler which is designed for this sort of application (https://sciagraph.com) but you can also try py-spy, which isn't as well designed for data processing/analysis applications (e.g. it won't visualize parallelism at all) but can still be informative (https://github.com/benfred/py-spy). Both are written in Rust ;)

Any advantages/disadvantages compared to py-spy [1]?

[1]: https://github.com/benfred/py-spy

Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.

A good profiler would be https://github.com/benfred/py-spy . If you run your app/benchmark with it, it should be able to draw a flamegraph telling you where the majority of time is spent. The info here is quite fine grained so it would already tell you where the bottleneck is. Without a full-fledged profiler you can also measure the timings in various parts of the code to understand where the bottleneck is.

Might be worth raising an issue on py-spy (a python profiler written in rust which "supports profiling native python extensions written in languages like C/C++ or Cython" to see if that can close the loop.